AI 中文总结
针对电力系统受大型周期性负荷驱动的强制振荡问题，采用无需显式模型的数据驱动预测控制（DeePC），在多机两区域系统上验证其阻尼效果优于传统PSS，凸显其在现代电力系统中的应用潜力。
AI 中文摘要
由数据中心等大型周期性负荷驱动的电力系统持续强制振荡，对依赖固定整定参数、适应性有限的传统电力系统稳定器（PSS）构成挑战。本文研究采用数据驱动替代方案——数据驱动预测控制（DeePC）来抑制此类振荡。DeePC无需显式系统模型，可直接从测量轨迹构建控制动作，从而适应变化的运行工况。我们在遭受强制振荡的多机两区域系统上评估DeePC的性能，并将其与传统PSS进行对比。研究考察了不同输入输出配置的影响，以及Hankel矩阵构建中代表性历史数据的作用。结果表明，DeePC可实现更优的阻尼效果，但其有效性关键取决于底层数据集的质量和代表性。这些发现凸显了数据驱动预测控制在动态特性不断变化且不确定的现代电力系统中，具备补充或超越传统稳定器的潜力。
英文摘要
Sustained forced oscillations in power systems, driven by large cyclic loads such as data centers, pose a challenge to conventional power system stabilizers (PSSs), which rely on fixed tuned parameters and limited adaptability. This paper investigates the use of Data-Enabled Predictive Control (DeePC) as a data-driven alternative for damping such oscillations. DeePC constructs control actions directly from measured trajectories without requiring an explicit system model, enabling adaptation to changing operating conditions. We evaluate the performance of DeePC on a multi-machine two-area system subject to forced oscillations and compare it against a conventional PSS. The study examines the impact of different input-output configurations and the role of representative historical data in the Hankel matrix construction. Results show that DeePC can achieve superior damping. However, its effectiveness depends critically on the quality and representativeness of the underlying dataset. These findings highlight the potential of data-driven predictive control to complement or outperform conventional stabilizers in modern power systems with evolving and uncertain dynamics.
